Rauschert, S and Raubenheimer, K and Melton, PE and Huang, RC, Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification, Clinical Epigenetics, 12, (1) Article 51. ISSN 1868-7083 (2020) [Refereed Article]
Copyright 2020 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Main Body: Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a sub-discipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles.
Conclusion: We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods.
|Item Type:||Refereed Article|
|Keywords:||machine learning, DNA methylation, epigenetics|
|Research Division:||Biological Sciences|
|Research Field:||Epigenetics (incl. genome methylation and epigenomics)|
|Objective Group:||Public health (excl. specific population health)|
|Objective Field:||Preventive medicine|
|UTAS Author:||Melton, PE (Dr Phillip Melton)|
|Web of Science® Times Cited:||7|
|Deposited By:||Menzies Institute for Medical Research|
|Downloads:||9 View Download Statistics|
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